Reinhard encoding, also known as Reinhard tone mapping, is a technique to reduce the dynamic range of high dynamic range (HDR) images, making them more suitable for Photo Realistic display on consumer devices. This technique was first introduced by Erik Reinhard and his colleagues in 2002.

So what’s that?

Some Tone mapping operator aims to preserve the local contrast and details of an HDR image while compressing its dynamic range. The technique is based on a simple yet effective idea: to adapt the tone mapping process to the local luminance of the image.

The Reinhard encoding operator works by applying a non-linear transformation to the luminance channel of the HDR image. The transformation is designed to compress the dynamic range of the image while preserving its local contrast and details.

Mathematical Formulation

Can be mathematically formulated as follows:

L Reinhard = (L / (L + L0^2)) * Lmax

where L is the luminance of the HDR image, L0 is a scaling factor, and Lmax is the maximum luminance of the display device.

How it works

The Reinhard encoding operator works by applying a non-linear transformation to the luminance channel of the HDR image. The transformation has two main components:

  1. Local Contrast Preservation: The operator preserves the local contrast of the image by applying a non-linear transformation to the luminance channel. This transformation ensures that the local contrast of the image is preserved, even in regions with high dynamic range.
  2. Dynamic Range Compression: The operator compresses the dynamic range of the image by applying a scaling factor to the luminance channel. This scaling factor reduces the overall dynamic range of the image, making it more suitable for display on conventional devices.

Pros

Several advantages over other tone mapping operators:

  1. Preserves Local Contrast: Even in regions with high dynamic range.
  2. Simple to Implement: Simple to code and can be applied to a wide range of images.
  3. Effective Dynamic Range Compression: Effectively compresses the dynamic range of HDR images, making them more suitable for display on conventional/slow devices.

Cons

Not so much downside to it, but there are potentially better alternatives:

  • Schlick’s tone mapping: Similar to Reinhard’s, older (1995) but it uses a more sophisticated curve to map the luminance values, supposedly for better ability to preserve more of the original contrast and color saturation.
  • Ward’s tone mapping: This algorithm is based on a psychophysical model of human vision and is designed to mimic the way the human eye adapts to different lighting conditions. Ward’s tone mapping can produce more natural-looking results, especially in scenes with a wide range of luminance values.
  • Pattanaik’s tone mapping: This algorithm is based on a multiscale model of human vision and is designed to preserve the local contrast and color information in the scene. Pattanaik’s tone mapping can produce results with more detailed textures and better color accuracy.
  • Krawczyk’s tone mapping: More recent development that uses a combination of local and global tone mapping techniques to produce, supposedly produces more natural-looking results.
  • Filmic tone mapping: Mimmic the way film cameras capture. Filmic tone mapping uses a non-linear curve to map the luminance values, which can produce a more cinematic look with better contrast and color saturation.

Further reading